1. Field of the Invention
The present invention relates to electronically-controlled motorcycle steering and maneuverability systems based on soft computing.
2. Description of the Related Art
Feedback control systems are widely used to maintain the output of a dynamic system at a desired value in spite of external disturbances that would displace it from the desired value. For example, a household space-heating furnace, controlled by a thermostat, is an example of a feedback control system. The thermostat continuously measures the air temperature inside the house, and when the temperature falls below a desired minimum temperature the thermostat turns the furnace on. When the interior temperature reaches the desired minimum temperature, the thermostat turns the furnace off. The thermostat-furnace system maintains the household temperature at a substantially constant value in spite of external disturbances such as a drop in the outside temperature. Similar types of feedback controls are used in many applications.
A central component in a feedback control system is a controlled object, a machine or a process that can be defined as a “plant”, whose output variable is to be controlled. In the above example, the “plant” is the house, the output variable is the interior air temperature in the house and the disturbance is the flow of heat (dispersion) through the walls of the house. The plant is controlled by a control system. In the above example, the control system is the thermostat in combination with the furnace. The thermostat-furnace system uses simple on-off feedback control system to maintain the temperature of the house. In many control environments, such as motor shaft position or motor speed control systems, simple on-off feedback control is insufficient. More advanced control systems rely on combinations of proportional feedback control, integral feedback control, and derivative feedback control. A feedback control based on a sum of proportional, plus integral, plus derivative feedback is often referred as a linear control. Similarly, Proportional feedback control is often referred to as P control, and Proportional plus Derivative feedback is often referred to as P(D) control, and Proportional plus Integral feedback is referred as P(I) control.
A linear control system (e.g., P, P(I), P(D), P(I)D, etc.) is based on a linear model of the plant. In classical control systems, a linear model is obtained in the form of ordinary differential equations. The plant is assumed to be relatively linear, time invariant, and stable. However, many real-world plants are time varying, highly non-linear, and unstable. For example, the dynamic model may contain parameters (e.g., masses, inductance, aerodynamics coefficients, etc.), which are either only approximately known or depend on a changing environment. If the parameter variation is small and the dynamic model is stable, then the linear controller may be satisfactory. However, if the parameter variation is large or if the dynamic model is unstable, then it is common to add Adaptive or Intelligent (AI) control functions to the linear control system.
AI control systems use an optimizer, typically a non-linear optimizer, to program the operation of the linear controller and thereby improve the overall operation of the control system.
Classical advanced control theory is based on the assumption that all controlled “plants” can be approximated as linear systems near equilibrium points. Unfortunately, this assumption is rarely true in the real world. Most plants are highly nonlinear, and often do not have simple control algorithms. In order to meet these needs for a nonlinear control, systems have been developed that use Soft Computing (SC) concepts such Fuzzy Neural Networks (FNN), Fuzzy Controllers (FC), and the like. By these techniques, the control system evolves (changes) in time to adapt itself to changes that may occur in the controlled “plant” and/or in the operating environment.
Control systems based on SC typically use a Knowledge Base (KB) to contain the knowledge of the FC system. The KB typically has many rules that describe how the SC determines control parameters during operation. Thus, the performance of an SC controller depends on the quality of the KB and the knowledge represented by the KB. Increasing the number of rules in the KB generally increases (very often with redundancy) the knowledge represented by the KB but at a cost of more storage and more computational complexity. Thus, design of a SC system typically involves tradeoffs regarding the size of the KB, the number of rules, the types of rules. etc. Unfortunately, the prior art methods for selecting KB parameters such as the number and types of rules are based on ad hoc procedures using intuition and trial-and-error approaches.
Steering and/or maneuverability control of a motorcycle using soft computing is particularly difficult because of the difficulty in obtaining a sufficiently optimal knowledge base (KB). If the KB does not contain enough knowledge about the dynamics of the motorcycle-rider system, then the soft computing controller will not be able to steer and/or maneuver the motorcycle in a satisfactory manner. At one level, increasing the knowledge conatined in the KB generally produces an increase in the size of the KB. A large KB is difficult to store and requires relatively large amounts of computational reasources in the soft computing controller. What is missing from the prior art is a system and method for controlling a motorcycle using a reduced-size KB that provides sufficient knowledge to provide good control.